Techniques of Thin-Section Microscopy

There are two commonly used methods of analyzing ceramics in thin-section. Modal analysis, or standard point counting, is preferred by many because it allows the modes (volumetric percentage) of identified rocks and minerals to be calculated with precision (Chayes and Fairbairn 1951; Chayes 1954; Heidke, Miksa and Wallace 2001; Lombard 1985; Middleton et al. 1985 ). In standard point counting a two- dimensional equidistance grid is first established, typically at an interval equal to or greater than the diameter of the largest inclusion in the thin-section. This procedure prevents single grains from being counted multiple times and further allows the reliability of the results to be calculated (e.g. ±5%) at a 95% confidence interval (2s) using the Van Der Plas and Tobi (1965) method. This is the technique used for our Basic and Advanced Ceramic Paste Characterization services (see Petrographic Services).

The second technique, or rather group of related techniques, is grain frequency counting, of which there are three approaches (Dickinson 2001; Middleton et al. 1985). The first is areal counting where a thin section is gradually moved below the crosshair eye-piece and all grains that fall completely within the field of view are identified, measured and counted. Second, is the ribbon (traverse) method where all inclusions that pass completely within a predefined transect, measured by the reticle in the microscope eyepiece, are size-graded, identified and tallied. Lastly is line counting where all grains that are intersected by the horizontal reticle are measured, identified and counted. Frequency counts do not produce volumetric estimates of grain proportions, but are quickly performed and comparable (Dickinson 2001). Moreover, ceramics with sparse and/or large (>.5 mm) inclusions cannot be economically analyzed by point-counting because the total number of identified inclusions is too small to allow for statistical comparisons. For instance, only 200 points can be counted on a thin-section of a sherd that has a maximum inclusion size of 2 mm. If the sample has a large volume of inclusions, say 25%, then only 50 inclusions could be counted for an optimal 20 mm x 40 mm sized thin-section. Similarly, a sample with only 5% very-fine to medium-sized inclusions would require a grid spacing of .5 mm and 2,000 counts in order to identify only 100 inclusions. Given these limitations, traverse counting is our preferred method for resource provenance studies.

Both techniques produce similar and replicable results for well-sorted sands. However, results of the two methods may differ significantly for poorly sorted sands (Dickinson 2001; Middleton et al. 1985), a problem that can be obviated by a research design focusing solely on certain grain sizes. Other methodological parameters can be altered, such as simply ignoring large grains during modal analysis allowing more points to be counted on a thin-section, or using a grid that is not equidistant. We encourage prospective customers to contact us with questions and concerns and assist us in devising an appropriate research design.

There are two commonly used methods of analyzing ceramics in thin-section. Modal analysis, or standard point counting, is preferred by many because it allows the modes (volumetric percentage) of identified rocks and minerals to be calculated with precision (Chayes and Fairbairn 1951; Chayes 1954; Heidke, Miksa and Wallace 2001; Lombard 1985; Middleton et al. 1985 ). In standard point counting a two- dimensional equidistance grid is first established, typically at an interval equal to or greater than the diameter of the largest inclusion in the thin-section. This procedure prevents single grains from being counted multiple times and further allows the reliability of the results to be calculated (e.g. ±5%) at a 95% confidence interval (2s) using the Van Der Plas and Tobi (1965) method. This is the technique used for our Basic and Advanced Ceramic Paste Characterization services (see Petrographic Services).

The second technique, or rather group of related techniques, is grain frequency counting, of which there are three approaches (Dickinson 2001; Middleton et al. 1985). The first is areal counting where a thin section is gradually moved below the crosshair eye-piece and all grains that fall completely within the field of view are identified, measured and counted. Second, is the ribbon (traverse) method where all inclusions that pass completely within a predefined transect, measured by the reticle in the microscope eyepiece, are size-graded, identified and tallied. Lastly is line counting where all grains that are intersected by the horizontal reticle are measured, identified and counted. Frequency counts do not produce volumetric estimates of grain proportions, but are quickly performed and comparable (Dickinson 2001). Moreover, ceramics with sparse and/or large (>.5 mm) inclusions cannot be economically analyzed by point-counting because the total number of identified inclusions is too small to allow for statistical comparisons. For instance, only 200 points can be counted on a thin-section of a sherd that has a maximum inclusion size of 2 mm. If the sample has a large volume of inclusions, say 25%, then only 50 inclusions could be counted for an optimal 20 mm x 40 mm sized thin-section. Similarly, a sample with only 5% very-fine to medium-sized inclusions would require a grid spacing of .5 mm and 2,000 counts in order to identify only 100 inclusions. Given these limitations, traverse counting is our preferred method for resource provenance studies.

Both techniques produce similar and replicable results for well-sorted sands. However, results of the two methods may differ significantly for poorly sorted sands (Dickinson 2001; Middleton et al. 1985), a problem that can be obviated by a research design focusing solely on certain grain sizes. Other methodological parameters can be altered, such as simply ignoring large grains during modal analysis allowing more points to be counted on a thin-section, or using a grid that is not equidistant. We encourage prospective customers to contact us with questions and concerns and assist us in devising an appropriate research design.

There are two basic approaches to “sourcing” archaeological ceramics through thin-section, polarized light microscopy (Heidke et al. 2001). The simplest method relies on the identification of one or more geographically restricted, easily distinguishable rocks or minerals. This approach, referred to as the “key grain” method (Dickinson and Shutler 1979; Heidke et al. 2001), though potentially effective and economical is only practicable where rare rocks or minerals are present. Examples of rare rocks and minerals in the Rocky Mountain west include anorthosite from the Laramie Anorthosite Complex in southeastern Wyoming (Page 2009, Stanley 1976) and leucite from southwestern Wyoming and north-central Montana, respectively.

The second approach to “sourcing” archaeological ceramics is the petrofacies method. This method is predicated on the principle that sand, or detritus, is composed of weathered or eroded particles of parent rock that have entered into a sedimentary transport system such as a stream. The mineral and lithic composition of sand within any stream basin reflects the mineral and lithic composition of the parent material from which it was derived. The petrofacies method was originally devised to identify and differentiate sandstones. It was quickly realized that the same principles that allow for the identification of sandstone petrofacies could be used to determine the provenance of sand-tempered pottery. Dickinson, (2001 and citations therein) in a series of studies conducted on archaeological ceramics from the South Pacific, showed that the provenance of sands within pottery can be determined with a relative degree of confidence. Lombard (1987), a student of Dickinson, later applied the petrofacies method to the Tucson Basin of southern Arizona where about 30 petrofacies have been identified in a relatively small area (Miksa et al. 2012). Unfortunately, despite the utility of the approach relatively few modern sand petrofacies have been systematically identified for archaeological applications. Lombard (1987) and Heidke et al. (2001) provide a thorough and useful outline of the process of creating a petrofacies model. Please contact us if you are interested in or would like assistance with developing a sand petrofacies model.

OWSA is currently working to build petrofacies models for the Front Range of Colorado, southeastern Wyoming and western Nebraska (Page 2009; Page and Reher 2013). Initial results indicate that there is patterned compositional variability between and within the North Platte, South Platte, and Arkansas River basins, as well as between and within several first and second order streams within the North and South Platte River drainages (Page 2009; Page and Reher 2013). As our database grows it may soon be possible to assign pots from eastern Colorado and Wyoming and perhaps Kansas and Nebraska to specific petrofacies.

The petrofacies approach is not suitable for all regions or ceramic traditions. Portions of the Central Plains of Nebraska and Kansas, for instance, have homogenous mineral compositions resulting from millions of years of lateral migration of a few large streams that drain the central Rockies. Sands in these regions are comprised predominantly of monomineralic quartz and feldspars grains with relatively few distinguishable lithic grains. The presence of key grains can in certain circumstances be used to determine where a sample did not originate (Page 2009), but specific provenance areas are not typically assignable. Similarly, grit (crushed rock), grog and shell tempered ceramics may not contain enough sand to allow for a provenance determination. Or, in the case of grit tempering the mineralogy of the rock that was crushed may mask the underlying mineral composition of the naturally occurring sands present in the paste.

Even in the absence of defined petrofacies models it is possible to glean valuable information from petrographic analyses (Ferring and Perttula 1987; Josephs 2011; Lintz and Reese-Taylor 1997; O’Malley 1981; Ownby 2012; Stoltman 1989, 1991). For instance, Page (2009) found that only 22.8% of a sample of 35 Central Plains tradition sherds from four sites in southeastern Wyoming was produced using sands that were locally available. The remaining 27 samples in Page’s study could not be assigned to a petrofacies but they could be identified as extralocal. Moreover, the results of the study called into question several hypotheses regarding the settlement pattern and nature of the Central Plains tradition (ca. AD 1000 – 1400) occupation of the High Plains (Page 2009). Similarly, another study using 19 Dismal River (protohistoric Plains Apache) sherds from one site in Nebraska (n=14) and four sites (n=5) in southeastern Wyoming revealed that pottery was produced from a variety of sources within the North and South Platte River basins (Page and Reher 2013). Again, the provenance of the samples could not identified with confidence, but the presence of pots not produced from locally available resources could be determined. Furthermore, the study undermined an often cited assertion that all micaceous pottery on the Plains was imported from the southwest (Page and Reher 2013). In short, the utility of petrographic studies is not limited to areas with defined petrofacies.

2013 A Petrographic Analysis of Dismal River Micaceous Pottery: Products of Southwestern Trade or Local Production? Paper presented at the 71st Annual Conference of the Plains Anthropological Association. Loveland, Colorado.

Solomon, M.

1963 Counting and Sampling Errors in Modal Analysis by Point Counter. Journal of Petrology 4:367-382.

Stoltman, James B.

1991 Ceramic Petrography as a Technique for Documenting Cultural Interaction: An Example from the Upper Mississippi Valley. American Antiquity 56:103-120.

Tweto, Ogden

1979 Geologic Map of Colorado. Geologic compilation cartography by R.E. Schoenfeld. Department of the Interior, United States Geological Survey prepared in cooperation with the Geological Survey of Colorado, Reston, Virginia.

Van Der Plas, L. and A. C. Tobi

1965 A Chart for Judging the Reliability of Point Counting Results. American Journal of Science 263: 87-90.